Artificial Intelligence for Management

The teaching methods (ME) to be used are balanced between traditional and active and are as follows:

ME1 Presentation of content by the teacher (compatible with learning objectives 1 to 7)

ME2 Testing of content learned by students (compatible with learning objectives 1 to 7)

ME3 Problem solving by students (compatible with learning objectives 4 to 8)

ME4 Interaction and sharing of ideas by students (compatible with learning objective 8)

ME5 Development of critical thinking by students (compatible with learning objectives 7 and 8)

ME6 Research carried out by students (compatible with learning objectives 4 to 8)

ME7 Project-based learning (compatible with learning objectives 8)

The curricular unit is based on theoretical-practical classes. The teaching methods (ME) to be used are balanced between traditional and active.

The classes include the presentation of concepts and methodologies and proceed to their discussion, as well as the demonstration of applied problem solving. In the classes, concepts and methodologies are presented, content is discussed and problem-solving is demonstrated. There will also be practical worksheets for students to complete and to test the content they have learned. The content is taught and discussed in a classroom environment.

In addition to traditional methods, the methodology will include active methods, namely project-based learning. As the name suggests, this is an active learning methodology that aims to associate learning with doing. This method is based on the collective construction of knowledge, moving away from the conventional classroom model where the teacher teaches a subject and the students show how much they have learned with a final assessment activity. . The project that is proposed to be developed, preferably carried out in a group, aims to go through the various phases of an artificial intelligence project for management. This project will encourage problem-solving by students, interaction and sharing of ideas by students in the same project group, the development of their critical thinking and will also promote research carried out by them in order to enhance the technological solution presented that is intended to assist management.

The main learning objectives (LO) defined are the following:

LO1 – Know the principles of artificial intelligence

LO2 – Know the principles of knowledge representation and inference

LO3 – Understand the operating principles of expert systems

LO4 – Know the main tasks and activities of artificial intelligence

LO5 – Know the main techniques and algorithms of artificial intelligence

LO6 – Know some tools and technologies and know how to use some of them

LO7 – Know how to evaluate the quality of solutions and know how to validate these solutions

LO8 – Know how to apply some of the main concepts and approaches learned in a practical project

The teaching methods (ME), based on theoretical-practical classes, integrate theory and practice, promoting the development of theoretical knowledge and practical skills and analytical competences. The teaching methodology includes several pedagogical methods (expository, demonstrative and project-based learning).

1 Introdução à inteligência artificial

1.1 História da inteligência artificial

1.2 Princípios da inteligência artificial, aprendizagem máquina e aprendizagem profunda

1.3 Inteligência artificial fraca, forte e superinteligência

1.4 Descoberta de conhecimento em bases de dados e mineração de dados

2 Conhecimento e inferência

3 Sistemas especialistas

4 Principais tarefas e atividades

4.1 Atividades preditivas (ou supervisionadas)

4.2 Atividades descritivas (ou não-supervisionadas)

4.3 Atividades prescritivas

5 Principais técnicas e algoritmos

5.1 Indução de árvores de decisão

5.2 Redes neuronais artificiais

5.3 Algoritmos genéticos

5.4 Indução de regras

5.5 Conjuntos difusos

5.6 Redes de Bayes

5.7 Outras técnicas e algoritmos

6 Ferramentas e Tecnologias

7 Qualidade e validação de soluções

NAO